Integrating Genomic Data and Genetic Risk Factors With AI for Predicting Susceptibility to Alzheimer's Disease-Reference-Cited by-同舟云学术

Integrating Genomic Data and Genetic Risk Factors With AI for Predicting Susceptibility to Alzheimer's Disease

Published:2024-06-28 Issue: Volume: Page:320-341
ISSN:2327-9354
Container-title:Advances in Medical Technologies and Clinical Practice
language:
Short-container-title:

Author:

Parveen Nikhat¹,D. Vidyabharathi²^ORCID,Shaik Nazeer³^ORCID,D. Ram Nivas⁴

Affiliation:

1. Koneru Lakshmaiah Education Foundation, India

2. Sona College of Technology, India

3. Srinivasa Ramanujan Institute of Technology (Autonomous), India

4. KPR Institute of Engineering and Technology, India

Abstract

Alzheimer's disease (AD) is a complicated neurodegenerative disease that has a vast effect on people, families, and society. despite a long time of research, there is no effective treatment for AD, highlighting the want for accurate and early prediction of disease susceptibility. recent advancements in genomic sequencing strategies have led to an explosion of facts, including genetic hazard variants associated with the advert. however, the widespread quantity of facts provides a mission in identifying meaningful patterns and predicting sickness chance. artificial intelligence (AI) has emerged as an effective device in integrating and studying massive genomic datasets, offering insights into complicated illnesses consisting of ad. In this evaluation, we speak about the contemporary country of integrating genomic records and genetic risk factors with AI for predicting susceptibility to AD. We can additionally explore the challenges and future directions in utilizing this approach for early detection and customized treatment of AD.

Publisher

IGI Global

Reference46 articles.

1. Classification of neurodegenerative disorders using machine learning techniques

2. Automatic Early Detection of Amyotrophic Lateral Sclerosis from Intelligible Speech Using Convolutional Neural Networks

3. Appakaya, S. B. (2021). An Automated Framework for Connected Speech Evaluation of Neurodegenerative Disease: A Case Study in Parkinson’s Disease [Doctoral dissertation, University of South Florida].

4. Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach

5. Baglat, P., Salehi, A. W., Gupta, A., & Gupta, G. (2020). Multiple machine learning models for detection of Alzheimer’s disease using OASIS dataset. In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, (pp. 614-622). Springer International Publishing.